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Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market

Author

Listed:
  • Danielsson, Jon

    (London School of Econ)

  • Morimoto, Yuji

    (Morgan Stanley Dean Witter)

Abstract

The various tools for risk measurement and management, especially for value-at-risk (VaR), are compared, with special emphasis on Japanese market data. Traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH-type methods are compared to extreme value theory (EVT). The distribution of extremes, asymmetry, clustering, and the dynamic structure of VaR all count as criteria for comparison of the various methods. We find that the GARCH class of models is not suitable for VaR forecasting for the sample data, due to both the inaccuracy and the high volatility of the VaR forecasts. In contrast, EVT forecasting of VaR resulted in much better VaR estimates, and more importantly, the EVT forecasts were considerably more stable, enhancing their practical applicability for Japanese market risk forecasts.

Suggested Citation

  • Danielsson, Jon & Morimoto, Yuji, 2000. "Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 25-48, December.
  • Handle: RePEc:ime:imemes:v:18:y:2000:i:2:p:25-48
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    File URL: http://www.imes.boj.or.jp/research/papers/english/me18-2-2.pdf
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    References listed on IDEAS

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    1. Holthausen, Robert W. & Leftwich, Richard W. & Mayers, David, 1987. "The effect of large block transactions on security prices: A cross-sectional analysis," Journal of Financial Economics, Elsevier, vol. 19(2), pages 237-267, December.
    2. Anil Bangia & Francis X. Diebold & Til Schuermann & John D. Stroughair, 1998. "Modeling Liquidity Risk With Implications for Traditional Market Risk Measurement and Management," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-062, New York University, Leonard N. Stern School of Business-.
    3. Bertsimas, Dimitris & Lo, Andrew W., 1998. "Optimal control of execution costs," Journal of Financial Markets, Elsevier, vol. 1(1), pages 1-50, April.
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    Citations

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    Cited by:

    1. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, Open Access Journal, vol. 2(2), pages 1-15, May.
    2. Luca Erzegovesi, 2002. "VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues," Alea Tech Reports 014, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    3. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    4. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo Group Munich.
    5. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," LSE Research Online Documents on Economics 66365, London School of Economics and Political Science, LSE Library.
    6. Nishiyama, N., 2001. "One idea of portfolio risk control for absolute return strategy risk adjustments by signals from correlation behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 457-472.
    7. Araújo Santos, P. & Fraga Alves, M.I., 2012. "A new class of independence tests for interval forecasts evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3366-3380.
    8. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    9. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    10. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    11. Raymond Knott & Marco Polenghi, 2006. "Assessing central counterparty margin coverage on futures contracts using GARCH models," Bank of England working papers 287, Bank of England.
    12. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2005. "Estimation of Value-at-Risk by extreme value and conventional methods: a comparative evaluation of their predictive performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(3), pages 209-228, July.
    13. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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